Overview

Dataset statistics

Number of variables20
Number of observations311
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.7 KiB
Average record size in memory160.4 B

Variable types

NUM16
CAT3
BOOL1

Warnings

T(C) has constant value "311" Constant
nAmor-Sl has constant value "311" Constant
Vol(aq) is highly correlated with b(H2O) and 2 other fieldsHigh correlation
b(H2O) is highly correlated with Vol(aq) and 2 other fieldsHigh correlation
nCa(aq) is highly correlated with b(H2O) and 2 other fieldsHigh correlation
nCa(s) is highly correlated with b(CaO)High correlation
b(CaO) is highly correlated with nCa(s)High correlation
nSi(aq) is highly correlated with b(H2O) and 2 other fieldsHigh correlation
nSi(s_reac) is highly correlated with b(SiO2) and 5 other fieldsHigh correlation
b(SiO2) is highly correlated with nSi(s_reac) and 5 other fieldsHigh correlation
mCSHQ is highly correlated with b(SiO2) and 5 other fieldsHigh correlation
nCa(CSHQ) is highly correlated with b(SiO2) and 5 other fieldsHigh correlation
nSi(CSHQ) is highly correlated with b(SiO2) and 5 other fieldsHigh correlation
nH2O(CSHQ) is highly correlated with b(SiO2) and 5 other fieldsHigh correlation
nGelPW(CSH) is highly correlated with b(SiO2) and 5 other fieldsHigh correlation
df_index has unique values Unique
b(CaO) has unique values Unique
b(SiO2) has unique values Unique
b(H2O) has unique values Unique
Vol(aq) has unique values Unique
nCa(aq) has unique values Unique
nCa(s) has unique values Unique
nSi(aq) has unique values Unique
nSi(s_reac) has unique values Unique
nPortlandite has unique values Unique
mCSHQ has unique values Unique
nCa(CSHQ) has unique values Unique
nSi(CSHQ) has unique values Unique
nH2O(CSHQ) has unique values Unique
nGelPW(CSH) has unique values Unique
ratio has unique values Unique

Reproduction

Analysis started2022-10-27 18:04:18.230821
Analysis finished2022-10-27 18:05:36.812603
Duration1 minute and 18.58 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.7524116
Minimum0
Maximum499
Zeros1
Zeros (%)0.3%
Memory size2.4 KiB
2022-10-27T13:05:37.128388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.5
Q1116.5
median240
Q3377
95-th percentile474.5
Maximum499
Range499
Interquartile range (IQR)260.5

Descriptive statistics

Standard deviation148.2154484
Coefficient of variation (CV)0.6031088259
Kurtosis-1.259879208
Mean245.7524116
Median Absolute Deviation (MAD)129
Skewness0.05936641193
Sum76429
Variance21967.81915
MonotocityStrictly increasing
2022-10-27T13:05:37.530389image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
010.3%
 
32610.3%
 
33910.3%
 
33710.3%
 
33610.3%
 
33210.3%
 
33110.3%
 
33010.3%
 
32810.3%
 
32510.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
010.3%
 
410.3%
 
510.3%
 
610.3%
 
810.3%
 
ValueCountFrequency (%) 
49910.3%
 
49810.3%
 
49710.3%
 
49410.3%
 
49210.3%
 

T(C)
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
25
311 
ValueCountFrequency (%) 
25311100.0%
 
2022-10-27T13:05:37.793170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-27T13:05:38.025809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:38.201811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

b(CaO)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.247051632
Minimum0.4532574
Maximum1.798218
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:38.475860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.4532574
5-th percentile0.64215075
Q10.99384645
median1.270771
Q31.5354455
95-th percentile1.7444735
Maximum1.798218
Range1.3449606
Interquartile range (IQR)0.54159905

Descriptive statistics

Standard deviation0.343908448
Coefficient of variation (CV)0.2757772326
Kurtosis-0.84685423
Mean1.247051632
Median Absolute Deviation (MAD)0.269102
Skewness-0.3021394029
Sum387.8330575
Variance0.1182730206
MonotocityNot monotonic
2022-10-27T13:05:38.824260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.57079510.3%
 
1.22046510.3%
 
1.13432710.3%
 
1.4092610.3%
 
1.03442210.3%
 
0.710793710.3%
 
1.74283510.3%
 
1.5942910.3%
 
1.62028410.3%
 
1.73294410.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.453257410.3%
 
0.462592610.3%
 
0.488034210.3%
 
0.507868710.3%
 
0.511423410.3%
 
ValueCountFrequency (%) 
1.79821810.3%
 
1.7947110.3%
 
1.79228510.3%
 
1.78874210.3%
 
1.78546310.3%
 

b(SiO2)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4153290309
Minimum0.2019985
Maximum0.6994621
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:39.183938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2019985
5-th percentile0.22238315
Q10.2992431
median0.3981634
Q30.5250296
95-th percentile0.6530628
Maximum0.6994621
Range0.4974636
Interquartile range (IQR)0.2257865

Descriptive statistics

Standard deviation0.1375298942
Coefficient of variation (CV)0.3311347967
Kurtosis-1.020014606
Mean0.4153290309
Median Absolute Deviation (MAD)0.1112738
Skewness0.3020488948
Sum129.1673286
Variance0.0189144718
MonotocityNot monotonic
2022-10-27T13:05:39.540603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.393695810.3%
 
0.567024210.3%
 
0.440809710.3%
 
0.595902510.3%
 
0.60784610.3%
 
0.279464710.3%
 
0.560084810.3%
 
0.312213310.3%
 
0.532366710.3%
 
0.663984610.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.201998510.3%
 
0.203848610.3%
 
0.204212610.3%
 
0.205202810.3%
 
0.207609410.3%
 
ValueCountFrequency (%) 
0.699462110.3%
 
0.695337710.3%
 
0.694451810.3%
 
0.688157310.3%
 
0.687265110.3%
 

b(H2O)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.60014891
Minimum2.777311
Maximum8.314109
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:39.855546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.777311
5-th percentile3.0406945
Q14.276007
median5.543634
Q37.037831
95-th percentile8.0503745
Maximum8.314109
Range5.536798
Interquartile range (IQR)2.761824

Descriptive statistics

Standard deviation1.6112515
Coefficient of variation (CV)0.2877158315
Kurtosis-1.201708447
Mean5.60014891
Median Absolute Deviation (MAD)1.393021
Skewness-0.03056086596
Sum1741.646311
Variance2.596131398
MonotocityNot monotonic
2022-10-27T13:05:40.190925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7.25589110.3%
 
6.66712910.3%
 
5.22692510.3%
 
8.00438810.3%
 
7.68943410.3%
 
4.15061310.3%
 
3.24920110.3%
 
8.10079610.3%
 
2.97369310.3%
 
2.82830110.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
2.77731110.3%
 
2.81547710.3%
 
2.82830110.3%
 
2.83236110.3%
 
2.85177110.3%
 
ValueCountFrequency (%) 
8.31410910.3%
 
8.2993110.3%
 
8.28858110.3%
 
8.26785410.3%
 
8.25042410.3%
 

Vol(aq)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06923503169
Minimum0.004780485
Maximum0.1281905
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:40.528221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.004780485
5-th percentile0.020028745
Q10.04322197
median0.06984818
Q30.094180165
95-th percentile0.1179886
Maximum0.1281905
Range0.123410015
Interquartile range (IQR)0.050958195

Descriptive statistics

Standard deviation0.03071028729
Coefficient of variation (CV)0.4435657288
Kurtosis-1.051039496
Mean0.06923503169
Median Absolute Deviation (MAD)0.0250976
Skewness-0.01589664265
Sum21.53209486
Variance0.0009431217455
MonotocityNot monotonic
2022-10-27T13:05:40.918040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0937803510.3%
 
0.0855579410.3%
 
0.0639545810.3%
 
0.105647310.3%
 
0.106458910.3%
 
0.0558105410.3%
 
0.0145510510.3%
 
0.110456410.3%
 
0.0124148310.3%
 
0.00478048510.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.00478048510.3%
 
0.011886110.3%
 
0.0124148310.3%
 
0.0138779210.3%
 
0.0145510510.3%
 
ValueCountFrequency (%) 
0.128190510.3%
 
0.128147310.3%
 
0.125757610.3%
 
0.125709910.3%
 
0.125273610.3%
 

pH
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
12.47261
310 
12.47262
 
1
ValueCountFrequency (%) 
12.4726131099.7%
 
12.4726210.3%
 
2022-10-27T13:05:41.203836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.3%
2022-10-27T13:05:41.389840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:41.546325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

nCa(aq)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001403291441
Minimum9.689269e-05
Maximum0.00259823
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:41.786665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum9.689269e-05
5-th percentile0.00040609985
Q10.00087604275
median0.001415719
Q30.001908879
95-th percentile0.002391453
Maximum0.00259823
Range0.00250133731
Interquartile range (IQR)0.00103283625

Descriptive statistics

Standard deviation0.0006224496066
Coefficient of variation (CV)0.4435640297
Kurtosis-1.051040096
Mean0.001403291441
Median Absolute Deviation (MAD)0.0005087
Skewness-0.01589400847
Sum0.4364236383
Variance3.874435127e-07
MonotocityNot monotonic
2022-10-27T13:05:42.106804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.00190078810.3%
 
0.00173413210.3%
 
0.00129626410.3%
 
0.00214131210.3%
 
0.00215776510.3%
 
0.00113119610.3%
 
0.000294927710.3%
 
0.0022387510.3%
 
0.000251629710.3%
 
9.689269e-0510.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
9.689269e-0510.3%
 
0.000240912910.3%
 
0.000251629710.3%
 
0.000281284110.3%
 
0.000294927710.3%
 
ValueCountFrequency (%) 
0.0025982310.3%
 
0.00259735610.3%
 
0.00254895910.3%
 
0.00254795210.3%
 
0.00253911910.3%
 

nCa(s)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.245648341
Minimum0.4506592
Maximum1.797213
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:42.433928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.4506592
5-th percentile0.6403331
Q10.9917615
median1.269137
Q31.535071
95-th percentile1.743907
Maximum1.797213
Range1.3465538
Interquartile range (IQR)0.5433095

Descriptive statistics

Standard deviation0.3441195082
Coefficient of variation (CV)0.2762573488
Kurtosis-0.8471004114
Mean1.245648341
Median Absolute Deviation (MAD)0.269371
Skewness-0.3018549474
Sum387.3966341
Variance0.1184182359
MonotocityNot monotonic
2022-10-27T13:05:42.807224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.56889410.3%
 
1.21873110.3%
 
1.13303110.3%
 
1.40711910.3%
 
1.03226410.3%
 
0.709662510.3%
 
1.7425410.3%
 
1.59205110.3%
 
1.62003210.3%
 
1.73284710.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.450659210.3%
 
0.460797310.3%
 
0.487304510.3%
 
0.507045510.3%
 
0.509940910.3%
 
ValueCountFrequency (%) 
1.79721310.3%
 
1.79402210.3%
 
1.79163410.3%
 
1.78701710.3%
 
1.78455510.3%
 

nSi(aq)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.14049257e-06
Minimum1.477955e-07
Maximum3.963186e-06
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:43.136783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.477955e-07
5-th percentile6.1922025e-07
Q11.336269e-06
median2.15944e-06
Q32.9116785e-06
95-th percentile3.647779e-06
Maximum3.963186e-06
Range3.8153905e-06
Interquartile range (IQR)1.5754095e-06

Descriptive statistics

Standard deviation9.494481122e-07
Coefficient of variation (CV)0.4435652454
Kurtosis0
Mean2.14049257e-06
Median Absolute Deviation (MAD)7.75937e-07
Skewness0
Sum0.0006656931893
Variance9.014517177e-13
MonotocityNot monotonic
2022-10-27T13:05:43.465801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.899352e-0610.3%
 
2.645109e-0610.3%
 
1.977248e-0610.3%
 
3.266203e-0610.3%
 
3.291318e-0610.3%
 
1.725465e-0610.3%
 
4.498663e-0710.3%
 
3.414906e-0610.3%
 
3.838218e-0710.3%
 
1.477955e-0710.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
1.477955e-0710.3%
 
3.67474e-0710.3%
 
3.838218e-0710.3%
 
4.290542e-0710.3%
 
4.498663e-0710.3%
 
ValueCountFrequency (%) 
3.963186e-0610.3%
 
3.961872e-0610.3%
 
3.887963e-0610.3%
 
3.886519e-0610.3%
 
3.872999e-0610.3%
 

nSi(s_reac)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.415326892
Minimum0.2019975
Maximum0.6994614
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:43.825649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2019975
5-th percentile0.22238135
Q10.29924075
median0.3981603
Q30.52502735
95-th percentile0.6530617
Maximum0.6994614
Range0.4974639
Interquartile range (IQR)0.2257866

Descriptive statistics

Standard deviation0.1375300289
Coefficient of variation (CV)0.3311368263
Kurtosis-1.020013071
Mean0.415326892
Median Absolute Deviation (MAD)0.1112726
Skewness0.3020493396
Sum129.1666634
Variance0.01891450884
MonotocityNot monotonic
2022-10-27T13:05:44.183668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.393692910.3%
 
0.567021610.3%
 
0.440807710.3%
 
0.595899210.3%
 
0.607842710.3%
 
0.27946310.3%
 
0.560084410.3%
 
0.312209910.3%
 
0.532366310.3%
 
0.663984510.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.201997510.3%
 
0.203846110.3%
 
0.204209210.3%
 
0.20519910.3%
 
0.20760810.3%
 
ValueCountFrequency (%) 
0.699461410.3%
 
0.695336610.3%
 
0.694449510.3%
 
0.688156710.3%
 
0.687262710.3%
 

nPortlandite
Real number (ℝ≥0)

UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.569902961
Minimum0.01374647
Maximum1.401833
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:44.520333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.01374647
5-th percentile0.074940455
Q10.268087
median0.5576201
Q30.8294967
95-th percentile1.1505705
Maximum1.401833
Range1.38808653
Interquartile range (IQR)0.5614097

Descriptive statistics

Standard deviation0.3502996138
Coefficient of variation (CV)0.6146653689
Kurtosis-0.9414819298
Mean0.569902961
Median Absolute Deviation (MAD)0.2829715
Skewness0.283827539
Sum177.2398209
Variance0.1227098194
MonotocityNot monotonic
2022-10-27T13:05:44.883558image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.928347810.3%
 
0.296175110.3%
 
0.415827510.3%
 
0.437578410.3%
 
0.0432916610.3%
 
0.254970510.3%
 
0.831271410.3%
 
1.08407910.3%
 
0.753861510.3%
 
0.652530910.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.0137464710.3%
 
0.0161042310.3%
 
0.0205708810.3%
 
0.0244616710.3%
 
0.0272699110.3%
 
ValueCountFrequency (%) 
1.40183310.3%
 
1.34166110.3%
 
1.33166310.3%
 
1.32695110.3%
 
1.28839910.3%
 

nAmor-Sl
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
311 
ValueCountFrequency (%) 
0311100.0%
 
2022-10-27T13:05:45.105799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

mCSHQ
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08438293913
Minimum0.0410403
Maximum0.1421112
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:45.290859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.0410403
5-th percentile0.045181745
Q10.06079744
median0.08089517
Q30.106671
95-th percentile0.13268405
Maximum0.1421112
Range0.1010709
Interquartile range (IQR)0.04587356

Descriptive statistics

Standard deviation0.02794229704
Coefficient of variation (CV)0.3311368071
Kurtosis-1.020013321
Mean0.08438293913
Median Absolute Deviation (MAD)0.02260751
Skewness0.3020491632
Sum26.24309407
Variance0.0007807719638
MonotocityNot monotonic
2022-10-27T13:05:45.637010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0799875110.3%
 
0.115203110.3%
 
0.0895599410.3%
 
0.121070210.3%
 
0.123496810.3%
 
0.0567791510.3%
 
0.113793710.3%
 
0.0634324210.3%
 
0.108162110.3%
 
0.134903310.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.041040310.3%
 
0.0414158910.3%
 
0.0414896610.3%
 
0.0416907710.3%
 
0.042180210.3%
 
ValueCountFrequency (%) 
0.142111210.3%
 
0.141273210.3%
 
0.141092910.3%
 
0.139814410.3%
 
0.139632810.3%
 

nCa(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6757453836
Minimum0.3286541
Maximum1.138038
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:45.993254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.3286541
5-th percentile0.361819
Q10.4868708
median0.647815
Q30.85423025
95-th percentile1.062545
Maximum1.138038
Range0.8093839
Interquartile range (IQR)0.36735945

Descriptive statistics

Standard deviation0.2237641823
Coefficient of variation (CV)0.3311368271
Kurtosis-1.020013102
Mean0.6757453836
Median Absolute Deviation (MAD)0.1810428
Skewness0.3020493688
Sum210.1568143
Variance0.05007040927
MonotocityNot monotonic
2022-10-27T13:05:46.368249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.640546410.3%
 
0.922555710.3%
 
0.717203210.3%
 
0.969540310.3%
 
0.988972610.3%
 
0.45469210.3%
 
0.911268710.3%
 
0.507971910.3%
 
0.866170910.3%
 
1.08031610.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.328654110.3%
 
0.331661810.3%
 
0.332252510.3%
 
0.333863110.3%
 
0.337782510.3%
 
ValueCountFrequency (%) 
1.13803810.3%
 
1.13132710.3%
 
1.12988410.3%
 
1.11964510.3%
 
1.1181910.3%
 

nSi(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.415326892
Minimum0.2019975
Maximum0.6994614
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:46.755282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2019975
5-th percentile0.22238135
Q10.29924075
median0.3981603
Q30.52502735
95-th percentile0.6530617
Maximum0.6994614
Range0.4974639
Interquartile range (IQR)0.2257866

Descriptive statistics

Standard deviation0.1375300289
Coefficient of variation (CV)0.3311368263
Kurtosis-1.020013071
Mean0.415326892
Median Absolute Deviation (MAD)0.1112726
Skewness0.3020493396
Sum129.1666634
Variance0.01891450884
MonotocityNot monotonic
2022-10-27T13:05:47.096108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.393692910.3%
 
0.567021610.3%
 
0.440807710.3%
 
0.595899210.3%
 
0.607842710.3%
 
0.27946310.3%
 
0.560084410.3%
 
0.312209910.3%
 
0.532366310.3%
 
0.663984510.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.201997510.3%
 
0.203846110.3%
 
0.204209210.3%
 
0.20519910.3%
 
0.20760810.3%
 
ValueCountFrequency (%) 
0.699461410.3%
 
0.695336610.3%
 
0.694449510.3%
 
0.688156710.3%
 
0.687262710.3%
 

nH2O(CSHQ)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.195332406
Minimum0.5813592
Maximum2.013086
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:47.434075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.5813592
5-th percentile0.6400251
Q10.86123035
median1.145926
Q31.511056
95-th percentile1.8795455
Maximum2.013086
Range1.4317268
Interquartile range (IQR)0.64982565

Descriptive statistics

Standard deviation0.3958185747
Coefficient of variation (CV)0.3311368225
Kurtosis-1.02001312
Mean1.195332406
Median Absolute Deviation (MAD)0.3202482
Skewness0.3020491403
Sum371.7483782
Variance0.156672344
MonotocityNot monotonic
2022-10-27T13:05:47.789474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.13306910.3%
 
1.63191710.3%
 
1.26866810.3%
 
1.71502910.3%
 
1.74940310.3%
 
0.80430910.3%
 
1.61195210.3%
 
0.898556310.3%
 
1.53217810.3%
 
1.91098110.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.581359210.3%
 
0.586679710.3%
 
0.587724610.3%
 
0.590573510.3%
 
0.597506610.3%
 
ValueCountFrequency (%) 
2.01308610.3%
 
2.00121510.3%
 
1.99866210.3%
 
1.98055110.3%
 
1.97797810.3%
 

C/S(CSHQ)
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
1.627021
272 
1.62702
39 
ValueCountFrequency (%) 
1.62702127287.5%
 
1.627023912.5%
 
2022-10-27T13:05:48.089930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-10-27T13:05:48.246422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:48.419531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.874598071
Min length7

nGelPW(CSH)
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5112072305
Minimum0.2486296
Maximum0.8609357
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:48.693797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.2486296
5-th percentile0.2737192
Q10.368322
median0.4900777
Q30.6462326
95-th percentile0.8038243
Maximum0.8609357
Range0.6123061
Interquartile range (IQR)0.2779106

Descriptive statistics

Standard deviation0.1692795395
Coefficient of variation (CV)0.3311368255
Kurtosis-1.020013065
Mean0.5112072305
Median Absolute Deviation (MAD)0.1369605
Skewness0.3020493599
Sum158.9854487
Variance0.02865556248
MonotocityNot monotonic
2022-10-27T13:05:48.996188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.484578910.3%
 
0.697921410.3%
 
0.542570410.3%
 
0.733465610.3%
 
0.748166310.3%
 
0.343978410.3%
 
0.689382710.3%
 
0.384285110.3%
 
0.655265810.3%
 
0.817268610.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
0.248629610.3%
 
0.25090510.3%
 
0.251351910.3%
 
0.252570310.3%
 
0.255535410.3%
 
ValueCountFrequency (%) 
0.860935710.3%
 
0.855858610.3%
 
0.854766710.3%
 
0.847021210.3%
 
0.845920810.3%
 

ratio
Real number (ℝ≥0)

UNIQUE

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.276037378
Minimum1.659257832
Maximum8.570108194
Zeros0
Zeros (%)0.0%
Memory size2.4 KiB
2022-10-27T13:05:49.324466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.659257832
5-th percentile1.781101795
Q12.223126053
median2.951144182
Q33.891740432
95-th percentile6.001247371
Maximum8.570108194
Range6.910850362
Interquartile range (IQR)1.668614379

Descriptive statistics

Standard deviation1.349438956
Coefficient of variation (CV)0.4119119534
Kurtosis1.136548736
Mean3.276037378
Median Absolute Deviation (MAD)0.7922375624
Skewness1.193059183
Sum1018.847625
Variance1.820985495
MonotocityNot monotonic
2022-10-27T13:05:49.621240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.98986984410.3%
 
2.15240372510.3%
 
2.57328048810.3%
 
2.3649170810.3%
 
1.70178301710.3%
 
2.54341138610.3%
 
3.11173415210.3%
 
5.10641282710.3%
 
3.04354874210.3%
 
2.60991595310.3%
 
Other values (301)30196.8%
 
ValueCountFrequency (%) 
1.65925783210.3%
 
1.67694373410.3%
 
1.67858544410.3%
 
1.68311229610.3%
 
1.68676525310.3%
 
ValueCountFrequency (%) 
8.57010819410.3%
 
7.90299472310.3%
 
7.50354273510.3%
 
7.26876891910.3%
 
7.0071825610.3%
 

Interactions

2022-10-27T13:04:21.996154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:22.245156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:22.571158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:22.825156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:23.069159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:23.317161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:23.566175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:23.861173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:24.171173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:24.435176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:25.036178image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:25.806176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:26.272180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:26.534181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:26.817182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:27.061187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:27.334188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:27.615189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:27.904190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:28.165193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:28.415196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:28.700197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:28.952201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:29.233200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:29.506200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:29.760204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:30.028206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:30.281208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:30.560208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:30.814212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:31.119214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:31.385218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:31.631217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:31.868219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:32.148216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:32.528224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:32.780227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:34.918237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:35.145241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:35.435241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:35.675245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:35.916246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:36.147754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:36.366507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:36.596576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:36.830954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:37.080959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:37.284084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:37.528244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:37.721878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:37.956244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:38.159375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:38.378116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:38.609457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:38.815688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:39.050057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:39.315684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:39.533630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:39.753343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:39.972086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:40.206470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:40.425216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:40.660646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:40.879397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:41.098149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:41.332528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:41.578527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:41.800120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:42.044946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:42.279328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:42.522544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:42.768929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:43.018922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:43.253309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:43.503300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:43.737613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:43.987625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:44.253262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:44.487617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:44.721633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:44.940365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:45.174741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:45.409126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:45.643523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:45.862283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:46.144690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:46.363454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:46.606262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:46.848723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:47.067465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:47.301842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:47.530834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:48.237925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:48.472305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:48.707752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:48.942121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:49.145247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:49.379634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:49.629053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:49.847792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:50.097807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:50.347814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:50.593331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:50.831211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:51.081204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:51.315587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:51.586659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:51.815929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:52.050294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:52.284675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:52.546275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:52.785306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:53.004054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:53.238430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:53.504055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:53.784945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:54.019324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:54.284957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:54.567306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:54.832201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:55.113442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:55.415004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:56.128852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:56.863228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:57.613232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:58.019487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:58.441364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:59.035694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:59.379438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:59.629214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:04:59.863588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:00.129220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:00.428467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:00.788681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:01.007432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:01.273064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:01.543345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:01.771965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:02.006344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:02.258882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:02.525040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:02.836476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:03.133348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:03.383349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:03.727167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:04.055292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:04.336545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:04.601846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:04.867470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:05.117473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:05.351847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:05.617754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:05.867760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:06.133396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:06.445889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:06.743812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:07.025031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:07.306302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:08.211082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:08.461089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:08.695318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:08.960936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:09.210933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:09.445330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:09.681608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:09.915999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:10.165998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:10.433069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:10.681579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:10.915953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:11.150320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:11.384713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:11.634994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:11.869386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:12.119374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:12.338126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:12.577718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:12.823232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:13.088835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:13.354465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:13.828254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:14.126262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:14.372504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:14.694397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:14.996000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:15.386002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:15.782009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:16.130010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:16.463785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:16.760799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:17.128500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:17.386642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:17.733897image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:17.980352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:18.230374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:18.547933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:18.793190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:19.027295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:19.261621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:19.496013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:19.778885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:20.080582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:20.373586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:20.719589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:21.014591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:21.292921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:21.585266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:21.871027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:22.149815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:22.533815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:22.958821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:23.234837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:23.505822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:23.803828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:24.074300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:24.324167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:24.595488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:24.824359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:25.074354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:25.324375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:25.605820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:25.855842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:26.137101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:26.417932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:26.668309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:26.902697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:27.168311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:27.418334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:27.653755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:27.903755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:28.153749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:28.388137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:28.668531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:28.918552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:29.184192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:29.449786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:29.714822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:29.949206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:30.199209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:30.433567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:31.523865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:31.778913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:32.044525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:32.294521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:32.526642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:32.762068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:32.980793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:33.215173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:33.465174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:33.715419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:33.965410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:34.184170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:34.418554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:34.637272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:34.856022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:35.090379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-10-27T13:05:50.012089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-27T13:05:50.824167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-27T13:05:51.543632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-27T13:05:52.111077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-10-27T13:05:52.581530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-10-27T13:05:35.653854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-10-27T13:05:36.499604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

df_indexT(C)b(CaO)b(SiO2)b(H2O)Vol(aq)pHnCa(aq)nCa(s)nSi(aq)nSi(s_reac)nPortlanditenAmor-SlmCSHQnCa(CSHQ)nSi(CSHQ)nH2O(CSHQ)C/S(CSHQ)nGelPW(CSH)ratio
0025.01.5707950.3936967.2558910.09378012.472610.0019011.5688942.899352e-060.3936930.9283480.00.0799880.6405460.3936931.1330691.6270210.4845793.989870
1425.00.9621640.2255685.0521940.06877112.472610.0013940.9607702.126388e-060.2255660.5937710.00.0458290.3670000.2255660.6491891.6270210.2776394.265524
2525.01.1931300.6520772.7773110.01387812.472610.0002811.1928494.290542e-070.6520770.1319070.00.1324841.0609420.6520771.8767101.6270200.8026121.829738
3625.01.4256870.5150546.3863010.07795412.472610.0015801.4241072.410088e-060.5150510.5861080.00.1046440.8379990.5150511.4823441.6270210.6339532.768036
4825.01.7461120.3905744.5244090.04135312.472610.0008381.7452741.278467e-060.3905731.1098040.00.0793540.6354700.3905731.1240891.6270210.4807394.470628
51025.00.6346480.2782544.2985300.05988512.472610.0012140.6334341.851423e-060.2782520.1807130.00.0565330.4527210.2782520.8008231.6270210.3424882.280826
61125.01.1081030.2676327.0824320.10185212.472610.0020641.1060393.148906e-060.2676290.6706010.00.0543750.4354370.2676290.7702491.6270210.3294124.140401
71225.01.1293750.5715565.2065360.06072212.472610.0012311.1281441.877285e-060.5715540.1982140.00.1161240.9299300.5715541.6449621.6270210.7035001.975966
81425.00.5371770.2842417.9882240.12814712.472610.0025970.5345803.961872e-060.2842370.0721200.00.0577490.4624600.2842370.8180501.6270210.3498551.889864
91525.01.2385250.6571813.5688880.02723912.472610.0005521.2379738.421293e-070.6571800.1687270.00.1335211.0692460.6571801.8913991.6270210.8088941.884602

Last rows

df_indexT(C)b(CaO)b(SiO2)b(H2O)Vol(aq)pHnCa(aq)nCa(s)nSi(aq)nSi(s_reac)nPortlanditenAmor-SlmCSHQnCa(CSHQ)nSi(CSHQ)nH2O(CSHQ)C/S(CSHQ)nGelPW(CSH)ratio
30148525.01.3072840.6383068.2885810.11166412.472610.0022631.3050213.452224e-060.6383030.2664890.00.1296861.0385320.6383031.8370691.6270210.7856582.048051
30248625.01.2520590.3231135.7835020.07454012.472610.0015111.2505482.304606e-060.3231100.7248410.00.0656470.5257070.3231100.9299281.6270210.3977023.874994
30348725.01.3542270.5940834.1350640.03680012.472610.0007461.3534811.137739e-060.5940820.3868970.00.1207010.9665840.5940821.7098001.6270210.7312292.279523
30448825.00.5912940.2988016.7445140.10437912.472610.0021160.5891783.227009e-060.2987970.1030290.00.0607070.4861490.2987970.8599541.6270210.3677761.978892
30548925.01.6628700.6513204.6171470.03864012.472610.0007831.6620871.194592e-060.6513190.6023780.00.1323301.0597090.6513191.8745291.6270210.8016792.553078
30649225.01.2707710.2038495.9891830.08061112.472610.0016341.2691372.492197e-060.2038460.9374750.00.0414160.3316620.2038460.5866801.6270210.2509056.233896
30749425.00.8958700.4689087.6141140.11074012.472610.0022450.8936253.423679e-060.4689050.1307070.00.0952690.7629180.4689051.3495331.6270210.5771541.910543
30849725.00.6054250.3460526.7611770.10335712.472610.0020950.6033303.195438e-060.3460490.0403020.00.0703080.5630280.3460490.9959461.6270210.4259361.749520
30949825.01.4210620.6569143.7063640.02643112.472610.0005361.4205268.171574e-070.6569130.3517150.00.1334671.0688120.6569131.8906311.6270210.8085652.163238
31049925.01.0385770.2643343.9119000.04592112.472610.0009311.0376461.419709e-060.2643330.6075710.00.0537050.4300750.2643330.7607641.6270210.3253563.929026